{"id":3517,"date":"2024-04-23T14:21:09","date_gmt":"2024-04-23T19:21:09","guid":{"rendered":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/?post_type=tribe_events&p=3517"},"modified":"2024-04-23T14:21:09","modified_gmt":"2024-04-23T19:21:09","slug":"lans-seminar-132","status":"publish","type":"tribe_events","link":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/event\/lans-seminar-132\/","title":{"rendered":"LANS Seminar"},"content":{"rendered":"

Seminar Title:<\/strong>Error\u2010controlled feature selection for ultrahigh\u2010dimensional and highly correlated feature spaces using deep learning \u2013 An application in neuroimaging studies<\/p>\n

Speaker:<\/strong>Arka Ganguli, Postdoctoral Researcher, Argonne National Laboratory, Specializing in Statistical Machine Learning<\/p>\n

Date\/Time:<\/strong> May 8, 2024\/ 10:30 AM-11:30 AM
\nLocation:\u00a0<\/strong>See Meeting URL on the cels-seminars website which will require an Argonne login.<\/em><\/p>\n

Description: <\/strong>Deep learning has been at the center of analytics in recent years due to its impressive empirical success in analyzing complex data objects. While its application in feature selection holds promise for uncovering insightful predictors, challenges persist, particularly in managing ultrahigh-dimensional, correlated features, and elevated noise levels\u00a0To bridge this gap, we propose a novel screening and cleaning method that integrates deep learning to achieve data-adaptive discovery of highly correlated predictors while controlling error rates. Extensive empirical evaluations across simulated scenarios and real datasets demonstrate our method’s efficacy in achieving high power while minimizing false discoveries.Transitioning to neuroimaging, we explore cognitive reserve\u2014a phenomenon observed in older adults maintaining cognitive abilities despite neuropathological diseases. Leveraging diffusion MRI tractography, we investigate subcortical white matter connections as potential cognitive reserve markers. However, traditional statistical analyses encounter challenges in handling tractography data’s high dimensionality and correlation. To overcome this, we introduce a flexible feature selection algorithm combining deep learning for cluster-level discovery with controlled error rates. Through simulations and application to clinical neuroimaging data, our approach reveals meaningful discoveries in brain regions linked to neurodegeneration risk and resilience. This integration of screening, cleaning, and deep learning offers a comprehensive solution for the neuroimaging study, facilitating deeper insights into cognitive reserve and related neurobiological substrates.<\/p>\n

Bio:\u00a0<\/strong>Arka Ganguli is a Postdoctoral Researcher at Argonne National Laboratory, specializing in statistical machine learning. He holds a Ph.D. in Statistics from Michigan State University, where his research focused on advanced statistical methodologies for feature selection in ultra-high dimensional datasets. Arka’s academic background includes a Bachelor’s and Master’s degrees in Statistics from the University of Calcutta, India. His research interests revolve around developing and applying statistical methods for analyzing high-dimensional datasets, encompassing feature selection, deep learning, and generative models.<\/p>\n

Please note that the meeting URL for this event can be seen on the cels-seminars website which requires an Argonne login.<\/em><\/p>\n

\n

See all upcoming talks at\u00a0https:\/\/www.anl.gov\/mcs\/lans-seminars<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Seminar Title:Error\u2010controlled feature selection for ultrahigh\u2010dimensional and highly correlated feature spaces using deep learning \u2013 An application in neuroimaging studies Speaker:Arka Ganguli, Postdoctoral Researcher, Argonne National Laboratory, Specializing in Statistical Machine Learning Date\/Time: May 8, 2024\/ 10:30 AM-11:30 AM Location:\u00a0See … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":976,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[2],"class_list":["post-3517","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminar","cat_seminar"],"acf":[],"_links":{"self":[{"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events\/3517","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/users\/976"}],"version-history":[{"count":2,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events\/3517\/revisions"}],"predecessor-version":[{"id":3520,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events\/3517\/revisions\/3520"}],"wp:attachment":[{"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/media?parent=3517"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tags?post=3517"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/wp-json\/wp\/v2\/tribe_events_cat?post=3517"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}